02_Frequencies
2025-03-11
library(CCA)
library(dplyr)
library(ggplot2)
library(reshape2)
library(ggridges)
library(gridExtra)
library(patchwork)
library(FactoMineR)Load
Loading Pre-processed data provided
## load
load(here::here("Data", "ProcessedData", "processed_AnalysisData.Rdata"))processed_data## # A tibble: 14,575 × 484
## fishNum dateSample dateTimeSample dateProcessed species spCode
## <chr> <date> <dttm> <date> <chr> <dbl>
## 1 LT001 2022-07-21 2022-07-21 16:56:00 2022-07-27 lakeTrout 81
## 2 LT001 2022-07-21 2022-07-21 16:56:00 2022-07-27 lakeTrout 81
## 3 LT001 2022-07-21 2022-07-21 16:56:00 2022-07-27 lakeTrout 81
## 4 LT001 2022-07-21 2022-07-21 16:56:00 2022-07-27 lakeTrout 81
## 5 LT001 2022-07-21 2022-07-21 16:56:00 2022-07-27 lakeTrout 81
## 6 LT001 2022-07-21 2022-07-21 16:56:00 2022-07-27 lakeTrout 81
## 7 LT001 2022-07-21 2022-07-21 16:56:00 2022-07-27 lakeTrout 81
## 8 LT001 2022-07-21 2022-07-21 16:56:00 2022-07-27 lakeTrout 81
## 9 LT001 2022-07-21 2022-07-21 16:56:00 2022-07-27 lakeTrout 81
## 10 LT001 2022-07-21 2022-07-21 16:56:00 2022-07-27 lakeTrout 81
## # ℹ 14,565 more rows
## # ℹ 478 more variables: totalLength <dbl>, forkLength <dbl>, weight <dbl>,
## # girth <dbl>, dorsoLatHeight <dbl>, clipTag <chr>, sex <chr>, mat <dbl>,
## # airbladderTotalLength <dbl>, airBladderWidth <dbl>, airbladderWeight <dbl>,
## # airBladderWeightCond <dbl>, agingStructure <chr>, tissueSample <chr>,
## # Region_name <chr>, FishTrack <chr>, MaxTSdiff <dbl>, Ping_time <chr>,
## # deltaRange <dbl>, deltaMinAng <dbl>, deltaMajAng <dbl>, …## variable names in data
names(processed_data)## [1] "fishNum" "dateSample"
## [3] "dateTimeSample" "dateProcessed"
## [5] "species" "spCode"
## [7] "totalLength" "forkLength"
## [9] "weight" "girth"
## [11] "dorsoLatHeight" "clipTag"
## [13] "sex" "mat"
## [15] "airbladderTotalLength" "airBladderWidth"
## [17] "airbladderWeight" "airBladderWeightCond"
## [19] "agingStructure" "tissueSample"
## [21] "Region_name" "FishTrack"
## [23] "MaxTSdiff" "Ping_time"
## [25] "deltaRange" "deltaMinAng"
## [27] "deltaMajAng" "aspectAngle"
## [29] "Target_range" "Angle_minor_axis"
## [31] "Angle_major_axis" "Distance_minor_axis"
## [33] "Distance_major_axis" "StandDev_Angles_Minor_Axis"
## [35] "StandDev_Angles_Major_Axis" "Target_true_depth"
## [37] "pingNumber" "Ping_S"
## [39] "Ping_E" "Num_targets"
## [41] "TS_mean" "Target_range_mean"
## [43] "Speed_4D_mean_unsmoothed" "Fish_track_change_in_range"
## [45] "Time_in_beam" "Distance_3D_unsmoothed"
## [47] "Thickness_mean" "Exclude_below_line_range_mean"
## [49] "Target_depth_mean" "Target_depth_max"
## [51] "Target_depth_min" "Fish_track_change_in_depth"
## [53] "Region_bottom_altitude_min" "Region_bottom_altitude_max"
## [55] "Region_bottom_altitude_mean" "Region_top_altitude_min"
## [57] "Region_top_altitude_max" "Region_top_altitude_mean"
## [59] "F45" "F45.5"
## [61] "F46" "F46.5"
## [63] "F47" "F47.5"
## [65] "F48" "F48.5"
## [67] "F49" "F49.5"
## [69] "F50" "F50.5"
## [71] "F51" "F51.5"
## [73] "F52" "F52.5"
## [75] "F53" "F53.5"
## [77] "F54" "F54.5"
## [79] "F55" "F55.5"
## [81] "F56" "F56.5"
## [83] "F57" "F57.5"
## [85] "F58" "F58.5"
## [87] "F59" "F59.5"
## [89] "F60" "F60.5"
## [91] "F61" "F61.5"
## [93] "F62" "F62.5"
## [95] "F63" "F63.5"
## [97] "F64" "F64.5"
## [99] "F65" "F65.5"
## [101] "F66" "F66.5"
## [103] "F67" "F67.5"
## [105] "F68" "F68.5"
## [107] "F69" "F69.5"
## [109] "F70" "F70.5"
## [111] "F71" "F71.5"
## [113] "F72" "F72.5"
## [115] "F73" "F73.5"
## [117] "F74" "F74.5"
## [119] "F75" "F75.5"
## [121] "F76" "F76.5"
## [123] "F77" "F77.5"
## [125] "F78" "F78.5"
## [127] "F79" "F79.5"
## [129] "F80" "F80.5"
## [131] "F81" "F81.5"
## [133] "F82" "F82.5"
## [135] "F83" "F83.5"
## [137] "F84" "F84.5"
## [139] "F85" "F85.5"
## [141] "F86" "F86.5"
## [143] "F87" "F87.5"
## [145] "F88" "F88.5"
## [147] "F89" "F89.5"
## [149] "F90" "F90.5"
## [151] "F91" "F91.5"
## [153] "F92" "F92.5"
## [155] "F93" "F93.5"
## [157] "F94" "F94.5"
## [159] "F95" "F95.5"
## [161] "F96" "F96.5"
## [163] "F97" "F97.5"
## [165] "F98" "F98.5"
## [167] "F99" "F99.5"
## [169] "F100" "F100.5"
## [171] "F101" "F101.5"
## [173] "F102" "F102.5"
## [175] "F103" "F103.5"
## [177] "F104" "F104.5"
## [179] "F105" "F105.5"
## [181] "F106" "F106.5"
## [183] "F107" "F107.5"
## [185] "F108" "F108.5"
## [187] "F109" "F109.5"
## [189] "F110" "F110.5"
## [191] "F111" "F111.5"
## [193] "F112" "F112.5"
## [195] "F113" "F113.5"
## [197] "F114" "F114.5"
## [199] "F115" "F115.5"
## [201] "F116" "F116.5"
## [203] "F117" "F117.5"
## [205] "F118" "F118.5"
## [207] "F119" "F119.5"
## [209] "F120" "F120.5"
## [211] "F121" "F121.5"
## [213] "F122" "F122.5"
## [215] "F123" "F123.5"
## [217] "F124" "F124.5"
## [219] "F125" "F125.5"
## [221] "F126" "F126.5"
## [223] "F127" "F127.5"
## [225] "F128" "F128.5"
## [227] "F129" "F129.5"
## [229] "F130" "F130.5"
## [231] "F131" "F131.5"
## [233] "F132" "F132.5"
## [235] "F133" "F133.5"
## [237] "F134" "F134.5"
## [239] "F135" "F135.5"
## [241] "F136" "F136.5"
## [243] "F137" "F137.5"
## [245] "F138" "F138.5"
## [247] "F139" "F139.5"
## [249] "F140" "F140.5"
## [251] "F141" "F141.5"
## [253] "F142" "F142.5"
## [255] "F143" "F143.5"
## [257] "F144" "F144.5"
## [259] "F145" "F145.5"
## [261] "F146" "F146.5"
## [263] "F147" "F147.5"
## [265] "F148" "F148.5"
## [267] "F149" "F149.5"
## [269] "F150" "F150.5"
## [271] "F151" "F151.5"
## [273] "F152" "F152.5"
## [275] "F153" "F153.5"
## [277] "F154" "F154.5"
## [279] "F155" "F155.5"
## [281] "F156" "F156.5"
## [283] "F157" "F157.5"
## [285] "F158" "F158.5"
## [287] "F159" "F159.5"
## [289] "F160" "F160.5"
## [291] "F161" "F161.5"
## [293] "F162" "F162.5"
## [295] "F163" "F163.5"
## [297] "F164" "F164.5"
## [299] "F165" "F165.5"
## [301] "F166" "F166.5"
## [303] "F167" "F167.5"
## [305] "F168" "F168.5"
## [307] "F169" "F169.5"
## [309] "F170" "F173"
## [311] "F173.5" "F174"
## [313] "F174.5" "F175"
## [315] "F175.5" "F176"
## [317] "F176.5" "F177"
## [319] "F177.5" "F178"
## [321] "F178.5" "F179"
## [323] "F179.5" "F180"
## [325] "F180.5" "F181"
## [327] "F181.5" "F182"
## [329] "F182.5" "F183"
## [331] "F183.5" "F184"
## [333] "F184.5" "F185"
## [335] "F185.5" "F186"
## [337] "F186.5" "F187"
## [339] "F187.5" "F188"
## [341] "F188.5" "F189"
## [343] "F189.5" "F190"
## [345] "F190.5" "F191"
## [347] "F191.5" "F192"
## [349] "F192.5" "F193"
## [351] "F193.5" "F194"
## [353] "F194.5" "F195"
## [355] "F195.5" "F196"
## [357] "F196.5" "F197"
## [359] "F197.5" "F198"
## [361] "F198.5" "F199"
## [363] "F199.5" "F200"
## [365] "F200.5" "F201"
## [367] "F201.5" "F202"
## [369] "F202.5" "F203"
## [371] "F203.5" "F204"
## [373] "F204.5" "F205"
## [375] "F205.5" "F206"
## [377] "F206.5" "F207"
## [379] "F207.5" "F208"
## [381] "F208.5" "F209"
## [383] "F209.5" "F210"
## [385] "F210.5" "F211"
## [387] "F211.5" "F212"
## [389] "F212.5" "F213"
## [391] "F213.5" "F214"
## [393] "F214.5" "F215"
## [395] "F215.5" "F216"
## [397] "F216.5" "F217"
## [399] "F217.5" "F218"
## [401] "F218.5" "F219"
## [403] "F219.5" "F220"
## [405] "F220.5" "F221"
## [407] "F221.5" "F222"
## [409] "F222.5" "F223"
## [411] "F223.5" "F224"
## [413] "F224.5" "F225"
## [415] "F225.5" "F226"
## [417] "F226.5" "F227"
## [419] "F227.5" "F228"
## [421] "F228.5" "F229"
## [423] "F229.5" "F230"
## [425] "F230.5" "F231"
## [427] "F231.5" "F232"
## [429] "F232.5" "F233"
## [431] "F233.5" "F234"
## [433] "F234.5" "F235"
## [435] "F235.5" "F236"
## [437] "F236.5" "F237"
## [439] "F237.5" "F238"
## [441] "F238.5" "F239"
## [443] "F239.5" "F240"
## [445] "F240.5" "F241"
## [447] "F241.5" "F242"
## [449] "F242.5" "F243"
## [451] "F243.5" "F244"
## [453] "F244.5" "F245"
## [455] "F245.5" "F246"
## [457] "F246.5" "F247"
## [459] "F247.5" "F248"
## [461] "F248.5" "F249"
## [463] "F249.5" "F250"
## [465] "F250.5" "F251"
## [467] "F251.5" "F252"
## [469] "F252.5" "F253"
## [471] "F253.5" "F254"
## [473] "F254.5" "F255"
## [475] "F255.5" "F256"
## [477] "F256.5" "F257"
## [479] "F257.5" "F258"
## [481] "F258.5" "F259"
## [483] "F259.5" "F260"Create a dataframe only containing frequencies.
frequency_data <- (
processed_data
|> select(1, 5, 59:481)
)
frequency_data |> head()## # A tibble: 6 × 425
## fishNum species F45 F45.5 F46 F46.5 F47 F47.5 F48 F48.5 F49 F49.5
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 LT001 lakeTrout -48.9 -47.5 -45.9 -44.2 -43.0 -41.7 -40.6 -39.6 -38.6 -37.7
## 2 LT001 lakeTrout -47.4 -47.6 -47.1 -45.5 -43.6 -41.6 -40.1 -39.0 -38.0 -37.2
## 3 LT001 lakeTrout -47.9 -49.3 -49.3 -47.2 -44.9 -42.7 -41.0 -39.5 -38.3 -37.2
## 4 LT001 lakeTrout -44.3 -45.7 -47.6 -48.8 -48.6 -46.7 -44.7 -42.9 -41.5 -40.3
## 5 LT001 lakeTrout -41.4 -42.3 -43.6 -44.7 -45.9 -46.6 -46.9 -46.7 -46.0 -45.1
## 6 LT001 lakeTrout -33.8 -33.7 -33.7 -33.5 -33.7 -33.8 -34.0 -34.1 -34.2 -34.2
## # ℹ 413 more variables: F50 <dbl>, F50.5 <dbl>, F51 <dbl>, F51.5 <dbl>,
## # F52 <dbl>, F52.5 <dbl>, F53 <dbl>, F53.5 <dbl>, F54 <dbl>, F54.5 <dbl>,
## # F55 <dbl>, F55.5 <dbl>, F56 <dbl>, F56.5 <dbl>, F57 <dbl>, F57.5 <dbl>,
## # F58 <dbl>, F58.5 <dbl>, F59 <dbl>, F59.5 <dbl>, F60 <dbl>, F60.5 <dbl>,
## # F61 <dbl>, F61.5 <dbl>, F62 <dbl>, F62.5 <dbl>, F63 <dbl>, F63.5 <dbl>,
## # F64 <dbl>, F64.5 <dbl>, F65 <dbl>, F65.5 <dbl>, F66 <dbl>, F66.5 <dbl>,
## # F67 <dbl>, F67.5 <dbl>, F68 <dbl>, F68.5 <dbl>, F69 <dbl>, F69.5 <dbl>, …Basic Frequencies
Data cleaning
Since we notice the data of F90 to F170 are missing, we want to remove these frequencies columns from our dataset.
## removing columns 93(F90) to 253(F170)
frequency_data <- frequency_data |> select(-c(93:253))
## name(frequency_data)Separate into three dataset for each species
LakeTrout <- frequency_data[frequency_data$species == "lakeTrout", ]
LakeWhiteFish <- frequency_data[frequency_data$species == "lakeWhitefish", ]
SmallmouthBass <- frequency_data[frequency_data$species == "smallmouthBass", ]TS Mean Between Fish Species
We want to use the mean of target strength across all ping times of each fish across all frequencies to explore the dataset by fish species. We first create a dataframe that contains all mean values as above.
ts_mean_allTime_data <- (
frequency_data
|> group_by(fishNum, species)
|> mutate(species = recode(species,
"lakeTrout" = "Lake Trout",
"lakeWhitefish" = "Lake Whitefish",
"smallmouthBass" = "Smallmouth Bass"))
|> summarize(across(starts_with("F"), ~mean(., na.rm = TRUE), .names = "{.col}_mean"))
)## `summarise()` has grouped output by 'fishNum'. You can override using the
## `.groups` argument.ts_mean_allTime_data |> head()## # A tibble: 6 × 264
## # Groups: fishNum [6]
## fishNum species F45_mean F45.5_mean F46_mean F46.5_mean F47_mean F47.5_mean
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 LT001 Lake Trout -46.3 -46.2 -46.3 -45.9 -45.4 -44.9
## 2 LT002 Lake Trout -47.7 -47.3 -47.0 -46.6 -46.6 -46.4
## 3 LT003 Lake Trout -47.3 -47.0 -46.8 -46.4 -46.3 -46.1
## 4 LT004 Lake Trout -40.4 -40.3 -40.3 -40.0 -40.0 -39.8
## 5 LT005 Lake Trout -60.5 -60.2 -60.1 -59.6 -59.7 -59.6
## 6 LT006 Lake Trout -39.3 -39.2 -39.2 -38.9 -38.8 -38.6
## # ℹ 256 more variables: F48_mean <dbl>, F48.5_mean <dbl>, F49_mean <dbl>,
## # F49.5_mean <dbl>, F50_mean <dbl>, F50.5_mean <dbl>, F51_mean <dbl>,
## # F51.5_mean <dbl>, F52_mean <dbl>, F52.5_mean <dbl>, F53_mean <dbl>,
## # F53.5_mean <dbl>, F54_mean <dbl>, F54.5_mean <dbl>, F55_mean <dbl>,
## # F55.5_mean <dbl>, F56_mean <dbl>, F56.5_mean <dbl>, F57_mean <dbl>,
## # F57.5_mean <dbl>, F58_mean <dbl>, F58.5_mean <dbl>, F59_mean <dbl>,
## # F59.5_mean <dbl>, F60_mean <dbl>, F60.5_mean <dbl>, F61_mean <dbl>, …We then melt the wide format dataframe into a long format dataframe for easy visualization.
ts_mean_allTime_data_long <- (
ts_mean_allTime_data
|> melt(
id.vars = c("fishNum", "species"),
variable.name = "Frequency",
value.name = "TS_mean_allTime"
)
)
ts_mean_allTime_data_long$Frequency <- as.numeric(gsub("F([0-9.]+)_mean", "\\1",
ts_mean_allTime_data_long$Frequency))
ts_mean_allTime_data_long <- ts_mean_allTime_data_long |> arrange(fishNum, Frequency)
ts_mean_allTime_data_long |> head()## fishNum species Frequency TS_mean_allTime
## 1 LT001 Lake Trout 45.0 -46.26963
## 2 LT001 Lake Trout 45.5 -46.18908
## 3 LT001 Lake Trout 46.0 -46.33088
## 4 LT001 Lake Trout 46.5 -45.91361
## 5 LT001 Lake Trout 47.0 -45.44261
## 6 LT001 Lake Trout 47.5 -44.92863Plots
We want to show different type of plots to discover the frequency patterns of each fish species.
First we want to plot an frequency response plot showing the mean target strength across different frequencies for different fish species with each fish response in the background.
(
ggplot(ts_mean_allTime_data_long, aes(x = Frequency, y = TS_mean_allTime, color = species))
## Indiviual fish (dotted lines)
+ geom_line(
aes(group = interaction(fishNum, species)),
linetype = "dotted",
alpha = 0.3,
linewidth = 0.5
)
## Mean trends (bold lines)
+ stat_summary(
fun = mean,
geom = "line",
aes(group = species),
linewidth = 1.2
)
## Aesthetics
+ scale_x_continuous(breaks = seq(40, 260, by = 20))
+ labs(
x = "Frequency (Hz)",
y = "Mean Target Strength (dB)",
color = "Fish Type"
)
+ theme(legend.position = "top")
)## Warning: Removed 172 rows containing non-finite outside the scale range
## (`stat_summary()`).## Warning: Removed 172 rows containing missing values or values outside the scale range
## (`geom_line()`).
Plot to observe the range of frequncies for each individual fish for each species.
(
ggplot(ts_mean_allTime_data_long, aes(x = Frequency, y = TS_mean_allTime, color = species))
+ stat_summary(fun.data = mean_cl_normal, geom = "ribbon", alpha = 0.2, aes(fill = species))
+ stat_summary(fun = mean, geom = "line", linewidth = 1.2)
)## Warning: Removed 172 rows containing non-finite outside the scale range
## (`stat_summary()`).
## Removed 172 rows containing non-finite outside the scale range
## (`stat_summary()`).
Plot to observe the range of frequencies of each species separtely.
(
ggplot(ts_mean_allTime_data_long, aes(x = Frequency, y = TS_mean_allTime))
+ geom_line(aes(group = fishNum), linetype = "dotted", alpha = 0.3)
+ stat_summary(fun = mean, geom = "line", color = "red", linewidth = 1.2)
+ facet_wrap(~species)
)## Warning: Removed 172 rows containing non-finite outside the scale range
## (`stat_summary()`).## Warning: Removed 172 rows containing missing values or values outside the scale range
## (`geom_line()`).
Density contour plot to show target strength measurements across differnt frequency ranges for each fish species.
## function to create density contour plot
create_frequency_density_plot <- function(data, title){
#' Create Target Strength Frequency Response Plots
#'
#' This function creates visualization of acoustic target strength data across frequency ranges for fish.
#' The plot combines multiple visualization elements:
#' - Hexagonal binning to show data density distribution
#' - Individual fish measurements as dotted gray lines
#' - Mean target strength trend line in red
#'
#' @param data A long format data frame containing columns in this order: fishNum, species, Frequency, and TS_mean
#' @param title String for the plot title
colnames(data)[4] <- "TS_mean"
(
ggplot(data, aes(x = Frequency, y = TS_mean))
## hexbin density layer
+ geom_hex(aes(fill = after_stat(count)), bins = 20, alpha = 0.7)
## Indiviiual fish lines
+ geom_line(
aes(group = interaction(fishNum, species)),
linetype = "dotted",
alpha = 0.2,
color = "gray20"
)
## mean trend
+ stat_summary(
fun = mean,
geom = "line",
color = "red",
linewidth = 1.2
)
## facet by fish type
+ facet_wrap(~species, nrow = 1)
## Aesthetics
+ scale_fill_viridis_c(name = "Data Density")
+ scale_x_continuous(breaks = seq(0, 260, by = 20))
+ labs(title = title, x = "Frequency (Hz)", y = "TS_mean (dB)")
# + theme_minimal()
+ theme(legend.position = "bottom")
)
}## split data into two frequency groups
ts_mean_allTime_low_long <- ts_mean_allTime_data_long |> filter(Frequency >= 45 & Frequency <= 89.5)
ts_mean_allTime_high_long <- ts_mean_allTime_data_long |> filter(Frequency >= 173 & Frequency <= 260)
# Create plots for both frequency ranges
plot_frequency_density_low <- create_frequency_density_plot(ts_mean_allTime_low_long, "Frequency Range: 45–89.5 Hz")
plot_frequency_density_high <- create_frequency_density_plot(ts_mean_allTime_high_long, "Frequency Range: 173–260 Hz")
# Arrange side-by-side
grid.arrange(plot_frequency_density_low, plot_frequency_density_high, ncol = 2)## Warning: Removed 172 rows containing non-finite outside the scale range
## (`stat_binhex()`).## Warning: Removed 172 rows containing non-finite outside the scale range
## (`stat_summary()`).## Warning: Removed 172 rows containing missing values or values outside the scale range
## (`geom_line()`).
Create plot of density distribution of target strength by fish species.
(
ggplot(ts_mean_allTime_data_long, aes(
x = TS_mean_allTime, # Numeric variable for density
y = species, # Categorical variable (fish species)
fill = species # Color by species
))
## density ridges
+ geom_density_ridges(alpha = 0.6, scale = 1.5)
+ labs(
title = "Density Distribution of TS_mean by Fish Species",
x = "Mean Target Strength (dB)",
y = "Fish Species"
)
+ theme_minimal()
+ scale_fill_manual(values = c("#1f77b4", "#ff7f0e", "#2ca02c"))
)## Picking joint bandwidth of 0.808## Warning: Removed 172 rows containing non-finite outside the scale range
## (`stat_density_ridges()`).
We create another density contour plot and we want to observe if there any potential dominant frequency response region by each fish species.
(
ggplot(ts_mean_allTime_data_long, aes(x = Frequency, y = TS_mean_allTime, color = species))
## density contours
+ geom_density_2d(aes(fill = species), alpha = 0.2, contour_var = "density")
## frequency response trend lines
+ geom_smooth(
method = "loess",
formula = y ~ x,
se = FALSE, # Remove confidence bands
linewidth = 1.2
)
## Aesthetics
+ labs(
title = "Dominant Regions and Frequency Response by Fish Species",
x = "Frequency (kHz)",
y = "Mean Target Strength (dB)",
color = "Fish Species"
)
+ scale_color_manual(values = c("#1f77b4", "#ff7f0e", "#2ca02c"))
+ scale_fill_manual(values = c("#1f77b4", "#ff7f0e", "#2ca02c"))
+ theme(legend.position = "bottom")
+ theme_minimal()
)
TS Mean In Fish Species
This section we want to investigate the potential trend / pattern in the frequency response in an indiviual fish species.
Lake Trout
We first want to investigate the frequency response across different
ping time for a single fish. We want to focus on
fishNum = LT001.
LT001_frequency <- LakeTrout[LakeTrout$fishNum == "LT004", ]
# LT001_frequency <- LakeWhiteFish[LakeWhiteFish$fishNum == "LWF003", ]
# LT001_frequency <- SmallmouthBass[SmallmouthBass$fishNum == "SMB005", ]## melt dataframe into plottable format
LT001_frequency$ping_id <- 1:nrow(LT001_frequency)
LT001_frequency_long <- melt(
LT001_frequency,
id.vars = c("fishNum", "species", "ping_id"),
variable.name = "Frequency",
value.name = "TS"
)
LT001_frequency_long$Frequency <- as.numeric(gsub("F", "",
LT001_frequency_long$Frequency))
LT001_frequency_long |> head()## fishNum species ping_id Frequency TS
## 1 LT004 lakeTrout 1 45 -41.64990
## 2 LT004 lakeTrout 2 45 -44.13450
## 3 LT004 lakeTrout 3 45 -47.54812
## 4 LT004 lakeTrout 4 45 -36.01969
## 5 LT004 lakeTrout 5 45 -35.59207
## 6 LT004 lakeTrout 6 45 -34.82420Plot to create visualization of frequency response of fish
LT001 across ping time.
(
ggplot(LT001_frequency_long, aes(x = Frequency, y = TS))
## Indiviual ping time response as dotted lines
+ geom_line(aes(group = ping_id), linetype = "dotted", alpha = 0.3, color = "gray40")
## mean respinse as solid line
+ stat_summary(fun = mean, geom = "line", color = "red", linewidth = 1.2)
+ stat_summary(fun = median, geom = "line", color = "blue", linewidth = 1.2)
## confidence interval for mean
# + stat_summary(fun.data = mean_cl_normal, geom = "ribbon", alpha = 0.2, fill = "red")
+ labs(
title = "Acoustic Frequency Response",
subtitle = "Individual ping responses with mean trend",
x = "Frequency (Hz)",
y = "Target Strength (dB)"
)
+ theme(
panel.grid.minor = element_blank(),
legend.position = "none"
)
+ theme_minimal()
)
Next, we want to compte the difference between the frequency response from the mean from different ping time.
## compute mean
LT001_frequency_mean <- (
LT001_frequency_long
|> group_by(Frequency)
|> summarize(TS_mean = mean(TS, na.rm = TRUE))
)
## left join and compute difference
LT001_frequency_long <- (
LT001_frequency_long
|> left_join(LT001_frequency_mean, by = "Frequency")
|> mutate(diff_from_mean = TS - TS_mean)
)
LT001_frequency_long |> head()## fishNum species ping_id Frequency TS TS_mean diff_from_mean
## 1 LT004 lakeTrout 1 45 -41.64990 -40.39746 -1.252438
## 2 LT004 lakeTrout 2 45 -44.13450 -40.39746 -3.737038
## 3 LT004 lakeTrout 3 45 -47.54812 -40.39746 -7.150657
## 4 LT004 lakeTrout 4 45 -36.01969 -40.39746 4.377772
## 5 LT004 lakeTrout 5 45 -35.59207 -40.39746 4.805394
## 6 LT004 lakeTrout 6 45 -34.82420 -40.39746 5.573259(
ggplot(LT001_frequency_long, aes(x = Frequency, y = diff_from_mean))
## Indiviual ping time response as dotted lines
+ geom_line(aes(group = ping_id), linetype = "dotted", alpha = 0.3, color = "gray40")
## mean respinse as solid line
+ stat_summary(fun = mean, geom = "line", color = "red", linewidth = 1.2)
+ stat_summary(fun = median, geom = "line", color = "blue", linewidth = 1.2)
## confidence interval for mean
# + stat_summary(fun.data = mean_cl_normal, geom = "ribbon", alpha = 0.2, fill = "red")
+ labs(
title = "Acoustic Frequency Response",
subtitle = "Individual ping responses with mean trend",
x = "Frequency (Hz)",
y = "Target Strength (dB)"
)
+ theme(
panel.grid.minor = element_blank(),
legend.position = "none"
)
+ theme_minimal()
)
# Calculate confidence intervals for the differences from mean
ci_data <- LT001_frequency_long %>%
group_by(Frequency) %>%
summarize(
mean_diff = mean(diff_from_mean, na.rm = TRUE),
sd_diff = sd(diff_from_mean, na.rm = TRUE),
n = n(),
# Calculate 95% confidence interval
ci_lower = mean_diff - qt(0.975, n-1) * sd_diff / sqrt(n),
ci_upper = mean_diff + qt(0.975, n-1) * sd_diff / sqrt(n),
# Calculate variability metrics
cv = sd_diff / abs(mean_diff + 0.0001), # Coefficient of variation (adding small constant to avoid div by 0)
range = max(diff_from_mean, na.rm = TRUE) - min(diff_from_mean, na.rm = TRUE)
)
# Create a visualization of the confidence intervals
ggplot(ci_data, aes(x = Frequency)) +
# Add confidence interval as ribbon
geom_ribbon(aes(ymin = ci_lower, ymax = ci_upper), alpha = 0.3, fill = "blue") +
# Add mean line
geom_line(aes(y = mean_diff), color = "blue", linewidth = 1) +
# Add zero reference line
geom_hline(yintercept = 0, linetype = "dashed", color = "red") +
# Add variability plot
# geom_line(aes(y = cv), color = "green", linewidth = 1) +
# Create second y-axis for coefficient of variation
scale_y_continuous(
name = "Difference from Mean (dB)",
sec.axis = sec_axis(~ ., name = "Coefficient of Variation")
) +
labs(
title = paste("Variation in Acoustic Response for", unique(LT001_frequency_long$fishNum)),
subtitle = "Blue: 95% CI of differences from mean, Green: Coefficient of Variation",
x = "Frequency (Hz)"
) +
theme_minimal() +
theme(
axis.title.y.right = element_text(color = "green"),
axis.text.y.right = element_text(color = "green")
)
Principal Component Analysis
We want to perform PCA in Lake Trout and we want to see if specified frequencies is contributing strongly to the variance of Lake Trout.
## aggregating dataframe into mean frquencies by each fish and scale
LakeTrout_agg <- (
LakeTrout
|> group_by(fishNum)
|> filter(fishNum != "LT008") ## contains missing bal
|> summarise(across(starts_with("F"), mean, na.rm = TRUE))
|> ungroup()
|> dplyr::select(-fishNum)
|> scale()
)## Warning: There was 1 warning in `summarise()`.
## ℹ In argument: `across(starts_with("F"), mean, na.rm = TRUE)`.
## ℹ In group 1: `fishNum = "LT001"`.
## Caused by warning:
## ! The `...` argument of `across()` is deprecated as of dplyr 1.1.0.
## Supply arguments directly to `.fns` through an anonymous function instead.
##
## # Previously
## across(a:b, mean, na.rm = TRUE)
##
## # Now
## across(a:b, \(x) mean(x, na.rm = TRUE))LakeTrout_pca <- PCA(LakeTrout_agg, graph = FALSE)
LakeTrout_pca$eig ## eigenvalue percentage of variance cumulative percentage of variance
## comp 1 244.21489472 93.211791879 93.21179
## comp 2 11.98462387 4.574283918 97.78608
## comp 3 1.27549045 0.486828415 98.27290
## comp 4 1.05141984 0.401305283 98.67421
## comp 5 0.91561696 0.349472122 99.02368
## comp 6 0.60554067 0.231122395 99.25480
## comp 7 0.41031229 0.156607745 99.41141
## comp 8 0.26497132 0.101134090 99.51255
## comp 9 0.23101183 0.088172453 99.60072
## comp 10 0.22164218 0.084596254 99.68531
## comp 11 0.17298864 0.066026199 99.75134
## comp 12 0.13547558 0.051708235 99.80305
## comp 13 0.12137783 0.046327415 99.84938
## comp 14 0.10342138 0.039473811 99.88885
## comp 15 0.09071791 0.034625156 99.92348
## comp 16 0.07759171 0.029615158 99.95309
## comp 17 0.06594847 0.025171171 99.97826
## comp 18 0.03107329 0.011860036 99.99012
## comp 19 0.02588105 0.009878265 100.00000LakeTrout_loadings_pc1 <-(
LakeTrout_pca$var$coord[, 1]
|> as.data.frame()
|> rename(Loading = "LakeTrout_pca$var$coord[, 1]")
|> mutate(Frequency = rownames(LakeTrout_pca$var$coord))
|> arrange(desc(abs(Loading)))
)
# loadings_pc1
LakeTrout_loadings_pc1_top_frequencies <- LakeTrout_loadings_pc1 |> head(20)
print(LakeTrout_loadings_pc1_top_frequencies)## Loading Frequency
## F192.5 0.9917246 F192.5
## F193 0.9911145 F193
## F192 0.9906835 F192
## F174.5 0.9906645 F174.5
## F186.5 0.9901207 F186.5
## F186 0.9898355 F186
## F185.5 0.9898325 F185.5
## F184.5 0.9898172 F184.5
## F219 0.9897545 F219
## F174 0.9895466 F174
## F201 0.9895126 F201
## F193.5 0.9894501 F193.5
## F191.5 0.9893288 F191.5
## F175 0.9888396 F175
## F173.5 0.9887690 F173.5
## F185 0.9887283 F185
## F187 0.9886458 F187
## F184 0.9884787 F184
## F191 0.9879446 F191
## F190.5 0.9878550 F190.5ggplot(LakeTrout_loadings_pc1_top_frequencies, aes(x = reorder(Frequency, -abs(Loading)), y = Loading)) +
geom_bar(stat = "identity", fill = "steelblue") +
labs(title = "Top Frequencies Driving Variability in LakeTrout",
x = "Frequency (kHz)", y = "PC1 Loading") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
cor_mat <- cor(LakeTrout_agg) |> as.data.frame()
cor_mat |> head()## F45 F45.5 F46 F46.5 F47 F47.5 F48
## F45 1.0000000 0.9996056 0.9984781 0.9963892 0.9938490 0.9907588 0.9873195
## F45.5 0.9996056 1.0000000 0.9994420 0.9977076 0.9952274 0.9922555 0.9889508
## F46 0.9984781 0.9994420 1.0000000 0.9991627 0.9970995 0.9944152 0.9912062
## F46.5 0.9963892 0.9977076 0.9991627 1.0000000 0.9992304 0.9974915 0.9950857
## F47 0.9938490 0.9952274 0.9970995 0.9992304 1.0000000 0.9993983 0.9979255
## F47.5 0.9907588 0.9922555 0.9944152 0.9974915 0.9993983 1.0000000 0.9995045
## F48.5 F49 F49.5 F50 F50.5 F51 F51.5
## F45 0.9854044 0.9867684 0.9875213 0.9879263 0.9864481 0.9846682 0.9823516
## F45.5 0.9872332 0.9884595 0.9890266 0.9890938 0.9875503 0.9859041 0.9838920
## F46 0.9899854 0.9910707 0.9913655 0.9911944 0.9896404 0.9880390 0.9861063
## F46.5 0.9942902 0.9948713 0.9945583 0.9936412 0.9917328 0.9898139 0.9879700
## F47 0.9972634 0.9976006 0.9969827 0.9954719 0.9931085 0.9906675 0.9892975
## F47.5 0.9990207 0.9990787 0.9982728 0.9964518 0.9938612 0.9912456 0.9897838
## F52 F52.5 F53 F53.5 F54 F54.5 F55
## F45 0.9784427 0.9752323 0.9749124 0.9758274 0.9793099 0.9835452 0.9865828
## F45.5 0.9802075 0.9773009 0.9769559 0.9776729 0.9810197 0.9851049 0.9878685
## F46 0.9829682 0.9805702 0.9800609 0.9805476 0.9836110 0.9872657 0.9894391
## F46.5 0.9852680 0.9833675 0.9826165 0.9825025 0.9848524 0.9877872 0.9891125
## F47 0.9868246 0.9849949 0.9844540 0.9838143 0.9851698 0.9869363 0.9871556
## F47.5 0.9870537 0.9850853 0.9841743 0.9827655 0.9833158 0.9843635 0.9839844
## F55.5 F56 F56.5 F57 F57.5 F58 F58.5
## F45 0.9882379 0.9880204 0.9868946 0.9849491 0.9826938 0.9790282 0.9742466
## F45.5 0.9893801 0.9891469 0.9880760 0.9865137 0.9845111 0.9811511 0.9767838
## F46 0.9904948 0.9904018 0.9896441 0.9886293 0.9868956 0.9840928 0.9805374
## F46.5 0.9894972 0.9889457 0.9883278 0.9880547 0.9869264 0.9850078 0.9824732
## F47 0.9869529 0.9858366 0.9851733 0.9853552 0.9846066 0.9832537 0.9813668
## F47.5 0.9833049 0.9818024 0.9810931 0.9815788 0.9811626 0.9803480 0.9789828
## F59 F59.5 F60 F60.5 F61 F61.5 F62
## F45 0.9726974 0.9729084 0.9747031 0.9755038 0.9760930 0.9759473 0.9755610
## F45.5 0.9755277 0.9758662 0.9774892 0.9781902 0.9786538 0.9784071 0.9779916
## F46 0.9798032 0.9803538 0.9819924 0.9827455 0.9832965 0.9831439 0.9827446
## F46.5 0.9826682 0.9838174 0.9856276 0.9865472 0.9872740 0.9870454 0.9864016
## F47 0.9822194 0.9841360 0.9863297 0.9876743 0.9888113 0.9887047 0.9880671
## F47.5 0.9805389 0.9831913 0.9858067 0.9875771 0.9891117 0.9890382 0.9884401
## F62.5 F63 F63.5 F64 F64.5 F65 F65.5
## F45 0.9749474 0.9763192 0.9770825 0.9752035 0.9721714 0.9702102 0.9690829
## F45.5 0.9773644 0.9785647 0.9791646 0.9773056 0.9743185 0.9721607 0.9707896
## F46 0.9819044 0.9827168 0.9829475 0.9809075 0.9778756 0.9758360 0.9744647
## F46.5 0.9853464 0.9856095 0.9853228 0.9830642 0.9798341 0.9779481 0.9766652
## F47 0.9870246 0.9868557 0.9862236 0.9840216 0.9806383 0.9788223 0.9773040
## F47.5 0.9876186 0.9873080 0.9864360 0.9839550 0.9801991 0.9782156 0.9766181
## F66 F66.5 F67 F67.5 F68 F68.5 F69
## F45 0.9693286 0.9694006 0.9693635 0.9701633 0.9729604 0.9749580 0.9735983
## F45.5 0.9705929 0.9702044 0.9698984 0.9707058 0.9736817 0.9761107 0.9755301
## F46 0.9743693 0.9737714 0.9735222 0.9744489 0.9774257 0.9796496 0.9793211
## F46.5 0.9770049 0.9764910 0.9762480 0.9772222 0.9800893 0.9819148 0.9816792
## F47 0.9775724 0.9774012 0.9769491 0.9778995 0.9807999 0.9823260 0.9818747
## F47.5 0.9771173 0.9776056 0.9775672 0.9787093 0.9817307 0.9832829 0.9828455
## F69.5 F70 F70.5 F71 F71.5 F72 F72.5
## F45 0.9713382 0.9687595 0.9662977 0.9653687 0.9674539 0.9686952 0.9681880
## F45.5 0.9738804 0.9715984 0.9692356 0.9684252 0.9705783 0.9715785 0.9708675
## F46 0.9777119 0.9753733 0.9730581 0.9723520 0.9745443 0.9755351 0.9748343
## F46.5 0.9798350 0.9772485 0.9749231 0.9742504 0.9764004 0.9774216 0.9770064
## F47 0.9796851 0.9768184 0.9742553 0.9732771 0.9755322 0.9765071 0.9761753
## F47.5 0.9804991 0.9774544 0.9747710 0.9737119 0.9758441 0.9766067 0.9762177
## F73 F73.5 F74 F74.5 F75 F75.5 F76
## F45 0.9643414 0.9665943 0.9708638 0.9712180 0.9683276 0.9643715 0.9647953
## F45.5 0.9668647 0.9686072 0.9723308 0.9721909 0.9689943 0.9648905 0.9653424
## F46 0.9709055 0.9720941 0.9751185 0.9743950 0.9712244 0.9674893 0.9683512
## F46.5 0.9733277 0.9742328 0.9767563 0.9756761 0.9725105 0.9692312 0.9706053
## F47 0.9720753 0.9730024 0.9757517 0.9748336 0.9720097 0.9691752 0.9708732
## F47.5 0.9719202 0.9726238 0.9750131 0.9737850 0.9708668 0.9682587 0.9703683
## F76.5 F77 F77.5 F78 F78.5 F79 F79.5
## F45 0.9670242 0.9686722 0.9695534 0.9688760 0.9672709 0.9637791 0.9592755
## F45.5 0.9680940 0.9699244 0.9710981 0.9702755 0.9682266 0.9642796 0.9592677
## F46 0.9718798 0.9737178 0.9749261 0.9740485 0.9719117 0.9679660 0.9626831
## F46.5 0.9742707 0.9756156 0.9766900 0.9757167 0.9735335 0.9697745 0.9645636
## F47 0.9746522 0.9757820 0.9768712 0.9759442 0.9737483 0.9699725 0.9651319
## F47.5 0.9742646 0.9754484 0.9765460 0.9756221 0.9733571 0.9695004 0.9644536
## F80 F80.5 F81 F81.5 F82 F82.5 F83
## F45 0.9532876 0.9511268 0.9517823 0.9556073 0.9574697 0.9581440 0.9602334
## F45.5 0.9528631 0.9508473 0.9519910 0.9560693 0.9583415 0.9594043 0.9619342
## F46 0.9556484 0.9535186 0.9545732 0.9583147 0.9600952 0.9605580 0.9625559
## F46.5 0.9573167 0.9552776 0.9561451 0.9593051 0.9602870 0.9599690 0.9610987
## F47 0.9582841 0.9566196 0.9573339 0.9599221 0.9599652 0.9587307 0.9587519
## F47.5 0.9575909 0.9559968 0.9564101 0.9584435 0.9579054 0.9564316 0.9562671
## F83.5 F84 F84.5 F85 F85.5 F86 F86.5
## F45 0.9590305 0.9571245 0.9573435 0.9556413 0.9552736 0.9567014 0.9565901
## F45.5 0.9612836 0.9596192 0.9601070 0.9586652 0.9583425 0.9598657 0.9597721
## F46 0.9620461 0.9602928 0.9609014 0.9597421 0.9595353 0.9612628 0.9612409
## F46.5 0.9603609 0.9585003 0.9593386 0.9586490 0.9585151 0.9606123 0.9607602
## F47 0.9574204 0.9554590 0.9562851 0.9555293 0.9550756 0.9575098 0.9580271
## F47.5 0.9549700 0.9530404 0.9540247 0.9535225 0.9531281 0.9556724 0.9565174
## F87 F87.5 F88 F88.5 F89 F89.5 F173
## F45 0.9574097 0.9598496 0.9656046 0.9687327 0.9695074 0.9686576 0.8813356
## F45.5 0.9604769 0.9624892 0.9676919 0.9703363 0.9702609 0.9686341 0.8836910
## F46 0.9620685 0.9640473 0.9690843 0.9719469 0.9715406 0.9696071 0.8889647
## F46.5 0.9616690 0.9637103 0.9686105 0.9718969 0.9714925 0.9696770 0.8917619
## F47 0.9591011 0.9610838 0.9665572 0.9701155 0.9699025 0.9681438 0.8913108
## F47.5 0.9578091 0.9601245 0.9658643 0.9696870 0.9695886 0.9676789 0.8911367
## F173.5 F174 F174.5 F175 F175.5 F176 F176.5
## F45 0.8814772 0.8810421 0.8834045 0.8798306 0.8670267 0.8489260 0.8490983
## F45.5 0.8839726 0.8833285 0.8849828 0.8815669 0.8688293 0.8506633 0.8509417
## F46 0.8891655 0.8878578 0.8880548 0.8845918 0.8722406 0.8541429 0.8545605
## F46.5 0.8915497 0.8893972 0.8882559 0.8849893 0.8730501 0.8546588 0.8552488
## F47 0.8899080 0.8869906 0.8858323 0.8827736 0.8707320 0.8517708 0.8524922
## F47.5 0.8891889 0.8856715 0.8842160 0.8813939 0.8693702 0.8499375 0.8507694
## F177 F177.5 F178 F178.5 F179 F179.5 F180
## F45 0.8640953 0.8766028 0.8846390 0.8806816 0.8717291 0.8623332 0.8631477
## F45.5 0.8655766 0.8782449 0.8862591 0.8829334 0.8746567 0.8658677 0.8669211
## F46 0.8685362 0.8809136 0.8886960 0.8864997 0.8795427 0.8717874 0.8733244
## F46.5 0.8685224 0.8803432 0.8876107 0.8862746 0.8805027 0.8738929 0.8755586
## F47 0.8650202 0.8762372 0.8834710 0.8823050 0.8766323 0.8703765 0.8722364
## F47.5 0.8627065 0.8735401 0.8806376 0.8795769 0.8739760 0.8683094 0.8704206
## F180.5 F181 F181.5 F182 F182.5 F183 F183.5
## F45 0.8654168 0.8704839 0.8677580 0.8695321 0.8748813 0.8826885 0.8861074
## F45.5 0.8691107 0.8734816 0.8702658 0.8715584 0.8764993 0.8842940 0.8875695
## F46 0.8757515 0.8794224 0.8759184 0.8769556 0.8814191 0.8893540 0.8920466
## F46.5 0.8779132 0.8808335 0.8775290 0.8784588 0.8828894 0.8914265 0.8936175
## F47 0.8743899 0.8776247 0.8748340 0.8762460 0.8812647 0.8900543 0.8924706
## F47.5 0.8724474 0.8753421 0.8726361 0.8740840 0.8792534 0.8882148 0.8909228
## F184 F184.5 F185 F185.5 F186 F186.5 F187
## F45 0.8902087 0.8899975 0.8860738 0.8879036 0.8827378 0.8788590 0.8749909
## F45.5 0.8913532 0.8910028 0.8871659 0.8889536 0.8842905 0.8805274 0.8769116
## F46 0.8955622 0.8951811 0.8913020 0.8928568 0.8886567 0.8848546 0.8813356
## F46.5 0.8968817 0.8968364 0.8932051 0.8943968 0.8906447 0.8863434 0.8827183
## F47 0.8954086 0.8951577 0.8910980 0.8922472 0.8889687 0.8847599 0.8815075
## F47.5 0.8939633 0.8940610 0.8902659 0.8914994 0.8885209 0.8842388 0.8811923
## F187.5 F188 F188.5 F189 F189.5 F190 F190.5
## F45 0.8670948 0.8736199 0.8741425 0.8752081 0.8774032 0.8768977 0.8797257
## F45.5 0.8688889 0.8750852 0.8753203 0.8762507 0.8786040 0.8783831 0.8814973
## F46 0.8731630 0.8788583 0.8789595 0.8798426 0.8823442 0.8824717 0.8853695
## F46.5 0.8743315 0.8797977 0.8801899 0.8811792 0.8840614 0.8852015 0.8881963
## F47 0.8732634 0.8784382 0.8789759 0.8800912 0.8832836 0.8851057 0.8886781
## F47.5 0.8730249 0.8782978 0.8790229 0.8801188 0.8835117 0.8858142 0.8896689
## F191 F191.5 F192 F192.5 F193 F193.5 F194
## F45 0.8781818 0.8836352 0.8860715 0.8896868 0.8883366 0.8853000 0.8757910
## F45.5 0.8801050 0.8854476 0.8875401 0.8909004 0.8897272 0.8869828 0.8776980
## F46 0.8839128 0.8886737 0.8903256 0.8939006 0.8934861 0.8914922 0.8822523
## F46.5 0.8867526 0.8911166 0.8924067 0.8961781 0.8963447 0.8947025 0.8854790
## F47 0.8869798 0.8906492 0.8915331 0.8956676 0.8963286 0.8947016 0.8854759
## F47.5 0.8879203 0.8911790 0.8916840 0.8957233 0.8961188 0.8939614 0.8850069
## F194.5 F195 F195.5 F196 F196.5 F197 F197.5
## F45 0.8725687 0.8690538 0.8647146 0.8586165 0.8520763 0.8417781 0.8461424
## F45.5 0.8740107 0.8700650 0.8657652 0.8592377 0.8529951 0.8426675 0.8470977
## F46 0.8778125 0.8728617 0.8683224 0.8611408 0.8556435 0.8453604 0.8497123
## F46.5 0.8805018 0.8747703 0.8699064 0.8619751 0.8569878 0.8464986 0.8506163
## F47 0.8807893 0.8750064 0.8700973 0.8616713 0.8564983 0.8453100 0.8489539
## F47.5 0.8805758 0.8749140 0.8702178 0.8616769 0.8562981 0.8446120 0.8480549
## F198 F198.5 F199 F199.5 F200 F200.5 F201
## F45 0.8469048 0.8442648 0.8436048 0.8546200 0.8658122 0.8707601 0.8738943
## F45.5 0.8479169 0.8452724 0.8445146 0.8554607 0.8666075 0.8718054 0.8750523
## F46 0.8505671 0.8479337 0.8470889 0.8577636 0.8687639 0.8742796 0.8777576
## F46.5 0.8514519 0.8481006 0.8466847 0.8567185 0.8680714 0.8745390 0.8786997
## F47 0.8497251 0.8456834 0.8440536 0.8534941 0.8653302 0.8727755 0.8773325
## F47.5 0.8488437 0.8443403 0.8425504 0.8515378 0.8633237 0.8711308 0.8757898
## F201.5 F202 F202.5 F203 F203.5 F204 F204.5
## F45 0.8672311 0.8678472 0.8590229 0.8540326 0.8499832 0.8479032 0.8438723
## F45.5 0.8683216 0.8689468 0.8602023 0.8549402 0.8508695 0.8486307 0.8442134
## F46 0.8713431 0.8722081 0.8634012 0.8579811 0.8538991 0.8514670 0.8465810
## F46.5 0.8730778 0.8743564 0.8651726 0.8597230 0.8556393 0.8530446 0.8477358
## F47 0.8720325 0.8732063 0.8632490 0.8576906 0.8533077 0.8511095 0.8464261
## F47.5 0.8705795 0.8716900 0.8613167 0.8558843 0.8515709 0.8492915 0.8446808
## F205 F205.5 F206 F206.5 F207 F207.5 F208
## F45 0.8438744 0.8530104 0.8576317 0.8559934 0.8598971 0.8641325 0.8581053
## F45.5 0.8439907 0.8531335 0.8576581 0.8560001 0.8596715 0.8641711 0.8584839
## F46 0.8460521 0.8550152 0.8593349 0.8573716 0.8603399 0.8649209 0.8596328
## F46.5 0.8472091 0.8558373 0.8594970 0.8572746 0.8601430 0.8653929 0.8607746
## F47 0.8461299 0.8545386 0.8575653 0.8550196 0.8583274 0.8641634 0.8598480
## F47.5 0.8445502 0.8526318 0.8553434 0.8526969 0.8560408 0.8622156 0.8585043
## F208.5 F209 F209.5 F210 F210.5 F211 F211.5
## F45 0.8436658 0.8337528 0.8364794 0.8436289 0.8498493 0.8563687 0.8546032
## F45.5 0.8439564 0.8338900 0.8366601 0.8440762 0.8505136 0.8573123 0.8558820
## F46 0.8450302 0.8343373 0.8364582 0.8437715 0.8500952 0.8577998 0.8572976
## F46.5 0.8463106 0.8345945 0.8351880 0.8421351 0.8485884 0.8575730 0.8581897
## F47 0.8455187 0.8334624 0.8332370 0.8399656 0.8463606 0.8558189 0.8566819
## F47.5 0.8445140 0.8322650 0.8314311 0.8379433 0.8444859 0.8542300 0.8552172
## F212 F212.5 F213 F213.5 F214 F214.5 F215
## F45 0.8574469 0.8608665 0.8598258 0.8585295 0.8571250 0.8631257 0.8639994
## F45.5 0.8588150 0.8621803 0.8606818 0.8591382 0.8575760 0.8634090 0.8644789
## F46 0.8607082 0.8641004 0.8612378 0.8588554 0.8568647 0.8626664 0.8643587
## F46.5 0.8621732 0.8654905 0.8610791 0.8572028 0.8542674 0.8596574 0.8617882
## F47 0.8607755 0.8641384 0.8593709 0.8545897 0.8505908 0.8554287 0.8572417
## F47.5 0.8594094 0.8626875 0.8575466 0.8520458 0.8470016 0.8509378 0.8524917
## F215.5 F216 F216.5 F217 F217.5 F218 F218.5
## F45 0.8699774 0.8655652 0.8544398 0.8476136 0.8406149 0.8480860 0.8670758
## F45.5 0.8707068 0.8665095 0.8552435 0.8485549 0.8418781 0.8490780 0.8675653
## F46 0.8717291 0.8687689 0.8577830 0.8512354 0.8448554 0.8513976 0.8686805
## F46.5 0.8706073 0.8692267 0.8585103 0.8521968 0.8458881 0.8520777 0.8686840
## F47 0.8666128 0.8659400 0.8557386 0.8503158 0.8445178 0.8507204 0.8669878
## F47.5 0.8624892 0.8628803 0.8533305 0.8485553 0.8432201 0.8493403 0.8651829
## F219 F219.5 F220 F220.5 F221 F221.5 F222
## F45 0.8802872 0.8748360 0.8632657 0.8626737 0.8535032 0.8512038 0.8523668
## F45.5 0.8805856 0.8748207 0.8630290 0.8624480 0.8539021 0.8517935 0.8532623
## F46 0.8813972 0.8758788 0.8644312 0.8642142 0.8567709 0.8545042 0.8554348
## F46.5 0.8816106 0.8763989 0.8651434 0.8653597 0.8588058 0.8562988 0.8565390
## F47 0.8797898 0.8743640 0.8628216 0.8633505 0.8570355 0.8547846 0.8552688
## F47.5 0.8778475 0.8722983 0.8607982 0.8617637 0.8557516 0.8537733 0.8541533
## F222.5 F223 F223.5 F224 F224.5 F225 F225.5
## F45 0.8552939 0.8669005 0.8771203 0.8898932 0.8924879 0.8868608 0.8736940
## F45.5 0.8557416 0.8663970 0.8763824 0.8891025 0.8920151 0.8866512 0.8738698
## F46 0.8563034 0.8647374 0.8745288 0.8873953 0.8910152 0.8866663 0.8746342
## F46.5 0.8559894 0.8623433 0.8719194 0.8852852 0.8901199 0.8873201 0.8760995
## F47 0.8538928 0.8594401 0.8685292 0.8819444 0.8876229 0.8856241 0.8747964
## F47.5 0.8521490 0.8565710 0.8649505 0.8786048 0.8846443 0.8830639 0.8725628
## F226 F226.5 F227 F227.5 F228 F228.5 F229
## F45 0.8633489 0.8433197 0.8393526 0.8378730 0.8374162 0.8377274 0.8339260
## F45.5 0.8635477 0.8433070 0.8394750 0.8382265 0.8373129 0.8372811 0.8334922
## F46 0.8640279 0.8430699 0.8385424 0.8369201 0.8355700 0.8355429 0.8313964
## F46.5 0.8648117 0.8424705 0.8368232 0.8350905 0.8342845 0.8352030 0.8308267
## F47 0.8630437 0.8391946 0.8322364 0.8305419 0.8309879 0.8337894 0.8302466
## F47.5 0.8607421 0.8363091 0.8290740 0.8277729 0.8288704 0.8322977 0.8287450
## F229.5 F230 F230.5 F231 F231.5 F232 F232.5
## F45 0.8467275 0.8565762 0.8656352 0.8576426 0.8609871 0.8572262 0.8440392
## F45.5 0.8463118 0.8562155 0.8656927 0.8583998 0.8618968 0.8579669 0.8445246
## F46 0.8438914 0.8539772 0.8642258 0.8586509 0.8623284 0.8579843 0.8437248
## F46.5 0.8424901 0.8525663 0.8632583 0.8591185 0.8628905 0.8585533 0.8429876
## F47 0.8412986 0.8507444 0.8603071 0.8552141 0.8594089 0.8558589 0.8397491
## F47.5 0.8391313 0.8481947 0.8577755 0.8530665 0.8572351 0.8538578 0.8377520
## F233 F233.5 F234 F234.5 F235 F235.5 F236
## F45 0.8333317 0.8366802 0.8408595 0.8462262 0.8568916 0.8535567 0.8490322
## F45.5 0.8338303 0.8366961 0.8407625 0.8457496 0.8561975 0.8528897 0.8481310
## F46 0.8324186 0.8341979 0.8380735 0.8428337 0.8530254 0.8497737 0.8452907
## F46.5 0.8300360 0.8302295 0.8338234 0.8393436 0.8500462 0.8474654 0.8438331
## F47 0.8260897 0.8254907 0.8288292 0.8354098 0.8469268 0.8450107 0.8414716
## F47.5 0.8236486 0.8222212 0.8251759 0.8324872 0.8442446 0.8426725 0.8393617
## F236.5 F237 F237.5 F238 F238.5 F239 F239.5
## F45 0.8407288 0.8307361 0.8302941 0.8371227 0.8303338 0.8195819 0.8048591
## F45.5 0.8398633 0.8299363 0.8297264 0.8370725 0.8308804 0.8199862 0.8049946
## F46 0.8380717 0.8287120 0.8286068 0.8365459 0.8310531 0.8199924 0.8047792
## F46.5 0.8373178 0.8286419 0.8283989 0.8363450 0.8307988 0.8195812 0.8042413
## F47 0.8346809 0.8263534 0.8260764 0.8336427 0.8280510 0.8174253 0.8024139
## F47.5 0.8325299 0.8244403 0.8244350 0.8318175 0.8260560 0.8151364 0.7999009
## F240 F240.5 F241 F241.5 F242 F242.5 F243
## F45 0.8012034 0.8119111 0.8232119 0.8205556 0.8241461 0.8246914 0.8189326
## F45.5 0.8008836 0.8112852 0.8221050 0.8196869 0.8238432 0.8253657 0.8195798
## F46 0.7999548 0.8093506 0.8196557 0.8183505 0.8240588 0.8271032 0.8206720
## F46.5 0.7987836 0.8072055 0.8173106 0.8170406 0.8239599 0.8282860 0.8207863
## F47 0.7962648 0.8037351 0.8139576 0.8133122 0.8206538 0.8250512 0.8175594
## F47.5 0.7929423 0.7998048 0.8097374 0.8094554 0.8172251 0.8220156 0.8143599
## F243.5 F244 F244.5 F245 F245.5 F246 F246.5
## F45 0.8008462 0.7913604 0.7917053 0.7797381 0.8038966 0.7972928 0.7809050
## F45.5 0.8018708 0.7921430 0.7922611 0.7796716 0.8041698 0.7975864 0.7815122
## F46 0.8036701 0.7936316 0.7927343 0.7795146 0.8037592 0.7972902 0.7816752
## F46.5 0.8044560 0.7945117 0.7924711 0.7785146 0.8018015 0.7952315 0.7799219
## F47 0.8011025 0.7901081 0.7875208 0.7741376 0.7977175 0.7916288 0.7761487
## F47.5 0.7981415 0.7873565 0.7845421 0.7708189 0.7941973 0.7884395 0.7729537
## F247 F247.5 F248 F248.5 F249 F249.5 F250
## F45 0.7592057 0.7496167 0.7466897 0.7476320 0.7650295 0.7769755 0.7854903
## F45.5 0.7594649 0.7497131 0.7468825 0.7476831 0.7649499 0.7769557 0.7853718
## F46 0.7598120 0.7499402 0.7474876 0.7476179 0.7640064 0.7764159 0.7849735
## F46.5 0.7584944 0.7483636 0.7469218 0.7475968 0.7632353 0.7763789 0.7849754
## F47 0.7548691 0.7442494 0.7426250 0.7435024 0.7593330 0.7722924 0.7810309
## F47.5 0.7520089 0.7415516 0.7405823 0.7419201 0.7573149 0.7703213 0.7791222
## F250.5 F251 F251.5 F252 F252.5 F253 F253.5
## F45 0.7966254 0.8005155 0.8128418 0.8139394 0.8129073 0.8135229 0.8364870
## F45.5 0.7967630 0.8008432 0.8132548 0.8152107 0.8139524 0.8146988 0.8372672
## F46 0.7972893 0.8017937 0.8140174 0.8158160 0.8140244 0.8146342 0.8359513
## F46.5 0.7978170 0.8019768 0.8136590 0.8152507 0.8135780 0.8144136 0.8344865
## F47 0.7940566 0.7976597 0.8100188 0.8120208 0.8101070 0.8116255 0.8323072
## F47.5 0.7925026 0.7959519 0.8081004 0.8105999 0.8087133 0.8106671 0.8311265
## F254 F254.5 F255 F255.5 F256 F256.5 F257
## F45 0.8500803 0.8512987 0.8462635 0.8490578 0.8640935 0.8672562 0.8587325
## F45.5 0.8508589 0.8523015 0.8478659 0.8511738 0.8655596 0.8681721 0.8602313
## F46 0.8507433 0.8533112 0.8498270 0.8531286 0.8659914 0.8677359 0.8602365
## F46.5 0.8505427 0.8535687 0.8508017 0.8532012 0.8638956 0.8651321 0.8586462
## F47 0.8478591 0.8506880 0.8468212 0.8487535 0.8594512 0.8613242 0.8562778
## F47.5 0.8469944 0.8498602 0.8456638 0.8467361 0.8564972 0.8589619 0.8551134
## F257.5 F258 F258.5
## F45 0.8309907 0.8401535 0.8497166
## F45.5 0.8328851 0.8414124 0.8504737
## F46 0.8333260 0.8414542 0.8501398
## F46.5 0.8323266 0.8405863 0.8494949
## F47 0.8315986 0.8391294 0.8475065
## F47.5 0.8312648 0.8386555 0.8468001Lake White Fish
PCA
is.na(LakeWhiteFish) |> sum()## [1] 0## aggregating dataframe into mean frquencies by each fish and scale
LakeWhiteFish_agg <- (
LakeWhiteFish
|> group_by(fishNum)
|> summarise(across(starts_with("F"), mean, na.rm = TRUE))
|> ungroup()
|> dplyr::select(-fishNum)
|> scale()
)
LakeWhiteFish_pca <- PCA(LakeWhiteFish_agg, graph = FALSE)
LakeWhiteFish_pca$eig## eigenvalue percentage of variance cumulative percentage of variance
## comp 1 231.9991409 88.54929044 88.54929
## comp 2 22.5857938 8.62053199 97.16982
## comp 3 2.9915858 1.14182662 98.31165
## comp 4 1.2695144 0.48454748 98.79620
## comp 5 1.2381232 0.47256612 99.26876
## comp 6 0.6835889 0.26091178 99.52967
## comp 7 0.5099489 0.19463698 99.72431
## comp 8 0.3544067 0.13526972 99.85958
## comp 9 0.2432860 0.09285724 99.95244
## comp 10 0.1246115 0.04756163 100.00000LakeWhiteFish_loadings_pc1 <-(
LakeWhiteFish_pca$var$coord[, 1]
|> as.data.frame()
|> rename(Loading = "LakeWhiteFish_pca$var$coord[, 1]")
|> mutate(Frequency = rownames(LakeWhiteFish_pca$var$coord))
|> arrange(desc(abs(Loading)))
)
# loadings_pc1
LakeWhiteFish_loadings_pc1_top_frequencies <- LakeWhiteFish_loadings_pc1 |> head(20)
print(LakeWhiteFish_loadings_pc1_top_frequencies)## Loading Frequency
## F177 0.9925150 F177
## F177.5 0.9923810 F177.5
## F181.5 0.9917162 F181.5
## F179 0.9916320 F179
## F176.5 0.9913896 F176.5
## F179.5 0.9913677 F179.5
## F178 0.9911983 F178
## F191.5 0.9910568 F191.5
## F192 0.9909740 F192
## F178.5 0.9909690 F178.5
## F182 0.9903404 F182
## F181 0.9902244 F181
## F187 0.9899987 F187
## F192.5 0.9897638 F192.5
## F189 0.9895014 F189
## F191 0.9894162 F191
## F180 0.9893366 F180
## F189.5 0.9893036 F189.5
## F180.5 0.9886951 F180.5
## F176 0.9886713 F176Smallmouth Bass
is.na(SmallmouthBass) |> sum()## [1] 0## aggregating dataframe into mean frquencies by each fish and scale
SmallmouthBass_agg <- (
SmallmouthBass
|> group_by(fishNum)
|> summarise(across(starts_with("F"), mean, na.rm = TRUE))
|> ungroup()
|> dplyr::select(-fishNum)
|> scale()
)
SmallmouthBass_pca <- PCA(SmallmouthBass_agg, graph = FALSE)
SmallmouthBass_pca$eig## eigenvalue percentage of variance cumulative percentage of variance
## comp 1 213.3640082 81.4366443 81.43664
## comp 2 23.9921634 9.1573143 90.59396
## comp 3 11.5097038 4.3930167 94.98698
## comp 4 3.9931711 1.5241111 96.51109
## comp 5 2.5775514 0.9837982 97.49488
## comp 6 2.0178735 0.7701807 98.26507
## comp 7 1.3838672 0.5281936 98.79326
## comp 8 1.1034893 0.4211791 99.21444
## comp 9 0.9147164 0.3491284 99.56357
## comp 10 0.6858171 0.2617623 99.82533
## comp 11 0.4576387 0.1746713 100.00000SmallmouthBass_loadings_pc1 <-(
SmallmouthBass_pca$var$coord[, 1]
|> as.data.frame()
|> rename(Loading = "SmallmouthBass_pca$var$coord[, 1]")
|> mutate(Frequency = rownames(SmallmouthBass_pca$var$coord))
|> arrange(desc(abs(Loading)))
)
# loadings_pc1
SmallmouthBass_loadings_pc1_top_frequencies <- SmallmouthBass_loadings_pc1 |> head(20)
print(SmallmouthBass_loadings_pc1_top_frequencies)## Loading Frequency
## F208 0.9897487 F208
## F209 0.9890043 F209
## F209.5 0.9878411 F209.5
## F208.5 0.9869398 F208.5
## F184.5 0.9859878 F184.5
## F210.5 0.9857557 F210.5
## F210 0.9845922 F210
## F212 0.9834752 F212
## F184 0.9829311 F184
## F212.5 0.9822498 F212.5
## F211 0.9818086 F211
## F185 0.9815360 F185
## F207.5 0.9813842 F207.5
## F183.5 0.9804217 F183.5
## F211.5 0.9798649 F211.5
## F205 0.9793735 F205
## F205.5 0.9790389 F205.5
## F213.5 0.9784301 F213.5
## F207 0.9775466 F207
## F214 0.9772022 F214(SmallmouthBass
|> group_by(fishNum)
|> summarise(across(starts_with("F"), mean, na.rm = TRUE))
|> ungroup()
|> dplyr::select(-fishNum)
|> summarise(across(starts_with("F"), mean, na.rm = TRUE))
)## # A tibble: 1 × 262
## F45 F45.5 F46 F46.5 F47 F47.5 F48 F48.5 F49 F49.5 F50 F50.5 F51
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 -53.9 -53.5 -53.2 -52.8 -52.6 -52.4 -52.4 -52.3 -52.2 -52.1 -51.9 -51.9 -51.8
## # ℹ 249 more variables: F51.5 <dbl>, F52 <dbl>, F52.5 <dbl>, F53 <dbl>,
## # F53.5 <dbl>, F54 <dbl>, F54.5 <dbl>, F55 <dbl>, F55.5 <dbl>, F56 <dbl>,
## # F56.5 <dbl>, F57 <dbl>, F57.5 <dbl>, F58 <dbl>, F58.5 <dbl>, F59 <dbl>,
## # F59.5 <dbl>, F60 <dbl>, F60.5 <dbl>, F61 <dbl>, F61.5 <dbl>, F62 <dbl>,
## # F62.5 <dbl>, F63 <dbl>, F63.5 <dbl>, F64 <dbl>, F64.5 <dbl>, F65 <dbl>,
## # F65.5 <dbl>, F66 <dbl>, F66.5 <dbl>, F67 <dbl>, F67.5 <dbl>, F68 <dbl>,
## # F68.5 <dbl>, F69 <dbl>, F69.5 <dbl>, F70 <dbl>, F70.5 <dbl>, F71 <dbl>, …Overall PCA
LT_mean_frequency <- (
LakeTrout
|> group_by(fishNum)
|> filter(fishNum != "LT008")
|> dplyr::select(matches("^F(1[7-9][0-9](\\.[0-9])?|2[0-5][0-9](\\.[0-9])?|260(\\.[0-9])?)$"))
|> summarise(across(starts_with("F"), mean, na.rm = TRUE))
|> ungroup()
|> dplyr::select(-fishNum)
|> summarise(across(starts_with("F"), mean, na.rm = TRUE))
)## Adding missing grouping variables: `fishNum`LT_mean_frequency |> head()## # A tibble: 1 × 172
## F173 F173.5 F174 F174.5 F175 F175.5 F176 F176.5 F177 F177.5 F178 F178.5
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 -48.9 -48.7 -48.5 -48.5 -48.5 -48.6 -48.8 -48.4 -48.1 -47.8 -47.5 -47.5
## # ℹ 160 more variables: F179 <dbl>, F179.5 <dbl>, F180 <dbl>, F180.5 <dbl>,
## # F181 <dbl>, F181.5 <dbl>, F182 <dbl>, F182.5 <dbl>, F183 <dbl>,
## # F183.5 <dbl>, F184 <dbl>, F184.5 <dbl>, F185 <dbl>, F185.5 <dbl>,
## # F186 <dbl>, F186.5 <dbl>, F187 <dbl>, F187.5 <dbl>, F188 <dbl>,
## # F188.5 <dbl>, F189 <dbl>, F189.5 <dbl>, F190 <dbl>, F190.5 <dbl>,
## # F191 <dbl>, F191.5 <dbl>, F192 <dbl>, F192.5 <dbl>, F193 <dbl>,
## # F193.5 <dbl>, F194 <dbl>, F194.5 <dbl>, F195 <dbl>, F195.5 <dbl>, …LWF_mean_frequency <- (
LakeWhiteFish
|> group_by(fishNum)
|> dplyr::select(matches("^F(1[7-9][0-9](\\.[0-9])?|2[0-5][0-9](\\.[0-9])?|260(\\.[0-9])?)$"))
|> summarise(across(starts_with("F"), mean, na.rm = TRUE))
|> ungroup()
|> dplyr::select(-fishNum)
|> summarise(across(starts_with("F"), mean, na.rm = TRUE))
)## Adding missing grouping variables: `fishNum`LWF_mean_frequency |> head()## # A tibble: 1 × 172
## F173 F173.5 F174 F174.5 F175 F175.5 F176 F176.5 F177 F177.5 F178 F178.5
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 -50.9 -50.6 -50.5 -50.5 -50.5 -50.8 -50.9 -50.5 -50.0 -49.7 -49.4 -49.5
## # ℹ 160 more variables: F179 <dbl>, F179.5 <dbl>, F180 <dbl>, F180.5 <dbl>,
## # F181 <dbl>, F181.5 <dbl>, F182 <dbl>, F182.5 <dbl>, F183 <dbl>,
## # F183.5 <dbl>, F184 <dbl>, F184.5 <dbl>, F185 <dbl>, F185.5 <dbl>,
## # F186 <dbl>, F186.5 <dbl>, F187 <dbl>, F187.5 <dbl>, F188 <dbl>,
## # F188.5 <dbl>, F189 <dbl>, F189.5 <dbl>, F190 <dbl>, F190.5 <dbl>,
## # F191 <dbl>, F191.5 <dbl>, F192 <dbl>, F192.5 <dbl>, F193 <dbl>,
## # F193.5 <dbl>, F194 <dbl>, F194.5 <dbl>, F195 <dbl>, F195.5 <dbl>, …SB_mean_frequency <- (
SmallmouthBass
|> group_by(fishNum)
|> dplyr::select(matches("^F(1[7-9][0-9](\\.[0-9])?|2[0-5][0-9](\\.[0-9])?|260(\\.[0-9])?)$"))
|> summarise(across(starts_with("F"), mean, na.rm = TRUE))
|> ungroup()
|> dplyr::select(-fishNum)
|> summarise(across(starts_with("F"), mean, na.rm = TRUE))
)## Adding missing grouping variables: `fishNum`SB_mean_frequency |> head()## # A tibble: 1 × 172
## F173 F173.5 F174 F174.5 F175 F175.5 F176 F176.5 F177 F177.5 F178 F178.5
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 -52.3 -52.1 -52.0 -52.1 -51.9 -52.1 -52.3 -52.2 -51.8 -51.4 -51.1 -51.0
## # ℹ 160 more variables: F179 <dbl>, F179.5 <dbl>, F180 <dbl>, F180.5 <dbl>,
## # F181 <dbl>, F181.5 <dbl>, F182 <dbl>, F182.5 <dbl>, F183 <dbl>,
## # F183.5 <dbl>, F184 <dbl>, F184.5 <dbl>, F185 <dbl>, F185.5 <dbl>,
## # F186 <dbl>, F186.5 <dbl>, F187 <dbl>, F187.5 <dbl>, F188 <dbl>,
## # F188.5 <dbl>, F189 <dbl>, F189.5 <dbl>, F190 <dbl>, F190.5 <dbl>,
## # F191 <dbl>, F191.5 <dbl>, F192 <dbl>, F192.5 <dbl>, F193 <dbl>,
## # F193.5 <dbl>, F194 <dbl>, F194.5 <dbl>, F195 <dbl>, F195.5 <dbl>, …## prepare fish species PCA comparison data
prepare_species_PCA_data <- function(species, mean_freq_df, loadings_df) {
## convery mean frequecy data to long format
species_long <- melt(
mean_freq_df,
variable.name = "Frequency",
value.name = "TS"
)
## removing "F" from frequency and make it numerical
species_long$Frequency <- as.numeric(gsub("F", "",
species_long$Frequency))
loadings_df$Frequency <- as.numeric(gsub("F", "",
loadings_df$Frequency))
## Add species name
species_long$Species <- species
## join with loadings
species_combined <- merge(
species_long,
loadings_df[, c("Frequency", "Loading")],
by = "Frequency",
all.x = TRUE
)
return(species_combined)
}## prepare PCA data for each species
LakeTrout_PCA_data <- prepare_species_PCA_data(
"Lake Trout",
LT_mean_frequency,
LakeTrout_loadings_pc1
)## No id variables; using all as measure variablesLakeWhiteFish_PCA_data <- prepare_species_PCA_data(
"Lake Whitefish",
LWF_mean_frequency,
LakeWhiteFish_loadings_pc1
)## No id variables; using all as measure variablesSmallmouthBass_PCA_data <- prepare_species_PCA_data(
"Smallmouth Bass",
SB_mean_frequency,
SmallmouthBass_loadings_pc1
)## No id variables; using all as measure variablesall_species_PCA_data <- rbind(
LakeTrout_PCA_data,
LakeWhiteFish_PCA_data,
SmallmouthBass_PCA_data
)all_species_PCA_data |> head()## Frequency TS Species Loading
## 1 173.0 -48.86743 Lake Trout 0.9871391
## 2 173.5 -48.68770 Lake Trout 0.9887690
## 3 174.0 -48.53021 Lake Trout 0.9895466
## 4 174.5 -48.46775 Lake Trout 0.9906645
## 5 175.0 -48.45928 Lake Trout 0.9888396
## 6 175.5 -48.63499 Lake Trout 0.9829606## create plots to show PCA laodings and TS_mean to visulize the PCA data
species_colors <- c(
"Lake Trout" = "#1f77b4",
"Lake Whitefish" = "#2ca02c",
"Smallmouth Bass" = "#ff7f0e"
)
## PCA loading plots
all_species_PCA_plot <- (
ggplot(all_species_PCA_data,
aes(x = Frequency, y = Loading, color = Species))
+ geom_line(linewidth = 0.5)
+ scale_color_manual(values = species_colors)
+ labs(
title = "PC1 Loadings by species",
y = "PC1 Loading"
)
+ theme(
axis.title.x = element_blank(),
axis.text.x = element_blank(),
legend.position = "none"
)
+ theme_minimal()
)
## TS_mean plots
all_species_TS_mean_plot <- (
ggplot(all_species_PCA_data,
aes(x = Frequency, y = TS, color = Species))
+ geom_line(linewidth = 0.8)
+ scale_color_manual(values = species_colors)
+ labs(
title = "Mean Target Strength by Species",
y = "Target Strength",
x = "Frequency"
)
+ theme(
axis.text.x = element_text(angle = 45, hjust = 1),
legend.position = "bottom"
)
+ theme_minimal()
)
## combined plots
all_species_PCA_combined_plot <- (
all_species_PCA_plot / all_species_TS_mean_plot
+ plot_layout(heights = c(1, 1.2))
)
all_species_PCA_combined_plot